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Multiscale quantum algorithms for quantum chemistry.

Huan Ma1, Jie Liu1, Honghui Shang2

  • 1Hefei National Laboratory, University of Science and Technology of China Hefei 230088 China liujie86@ustc.edu.cn jlyang@ustc.edu.cn.

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|March 27, 2023
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Summary
This summary is machine-generated.

This study introduces multiscale quantum computing to simulate complex materials and molecules. By combining classical and quantum methods, it addresses the limitations of current quantum devices for practical applications in chemistry and materials science.

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Area of Science:

  • Quantum computing
  • Computational chemistry
  • Materials science

Background:

  • Quantum advantage demonstrated via Gaussian boson sampling fuels interest in quantum computing for material design and drug discovery.
  • Current quantum resource demands for molecular simulations exceed near-term quantum device capabilities.

Purpose of the Study:

  • To propose a multiscale quantum computing framework for simulating complex systems.
  • To integrate classical and quantum computational methods efficiently.
  • To enable simulations beyond the capacity of current quantum hardware.

Main Methods:

  • Developed a multiscale quantum computing approach integrating various computational methods at different resolutions.
  • Implemented a near-term scheme combining adaptive variational quantum eigensolver algorithms, second-order Møller-Plesset perturbation theory, and Hartree-Fock theory.
  • Utilized the many-body expansion fragmentation approach.

Main Results:

  • Successfully applied the new algorithm to model systems with hundreds of orbitals.
  • Achieved decent accuracy on a classical simulator.
  • Demonstrated the feasibility of the multiscale approach for complex simulations.

Conclusions:

  • The proposed multiscale quantum computing framework offers a viable path for tackling complex material and biochemistry problems.
  • This work encourages further research into practical quantum computing applications for scientific discovery.
  • The integration of classical and quantum methods effectively manages quantum resource requirements.